


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[str]:
   UpperCAmelCase      					=  os.path.join(args.tf_model_dir	,    """parameters.json""" )
   UpperCAmelCase      					=  json.loads(open(UpperCamelCase_ ).read() )
   if not params:
      raise ValueError(
          F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
   if not args.output.endswith(""".pt""" ):
      UpperCAmelCase      					=  args.output + """.pt"""
   UpperCAmelCase      					=  OrderedDict()
   with tf.device("""/CPU:0""" ):
      UpperCAmelCase      					=  tf.train.load_checkpoint(args.tf_model_dir )
      UpperCAmelCase      					=  reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         UpperCAmelCase      					=  reader.get_tensor(UpperCamelCase_ ).astype(np.floataa )
         if key_name.endswith("""/adam_m""" ) or key_name.endswith("""/adam_v""" ):
            continue
         if key_name.startswith("""pasts/""" ):
            if key_name.startswith("""pasts/mlp""" ):
               UpperCAmelCase      					=  int(key_name[9] )
            elif key_name.startswith("""pasts/out""" ):
               UpperCAmelCase      					=  8
            UpperCAmelCase      					=  """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
            UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/moe""" ):
            UpperCAmelCase      					=  int(key_name[9:].split("""/""" )[0] )
            if key_name.endswith("""/switch_gating/kernel""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
               UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/softmlp/kernel""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
               UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/wo/kernel""" ) or key_name.endswith("""/wi/kernel""" ):
               UpperCAmelCase      					=  key_name[-9:-7]
               for i in range(1_6 ):
                  UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                  UpperCAmelCase      					=  (
                      vnp[i].transpose([1, 0] ).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/mlp""" ):
            UpperCAmelCase      					=  int(key_name[9:].split("""/""" )[0] )
            if key_name.endswith("""/p1/kernel""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
               UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/p1/bias""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/p2/kernel""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
               UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/p2/bias""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/ln""" ):
            UpperCAmelCase      					=  int(key_name[8:].split("""/""" )[0] )
            if key_name.endswith("""/b""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.norm.bias""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/g""" ):
               UpperCAmelCase      					=  """model.blocks.%d.feed_forward.norm.weight""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/att""" ):
            UpperCAmelCase      					=  int(key_name[9:].split("""/""" )[0] )
            if key_name.endswith("""/qkv/kernel""" ):
               UpperCAmelCase      					=  vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               UpperCAmelCase      					=  state[:, 0, :, :]
               UpperCAmelCase      					=  state[:, 1, :, :]
               UpperCAmelCase      					=  state[:, 2, :, :]
               UpperCAmelCase      					=  (
                   state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
                   .transpose([1, 0] )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  (
                   state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
                   .transpose([1, 0] )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  (
                   state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
                   .transpose([1, 0] )
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/o/kernel""" ):
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
               UpperCAmelCase      					=  (
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/an""" ):
            UpperCAmelCase      					=  int(key_name[8:].split("""/""" )[0] )
            if key_name.endswith("""/b""" ):
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.norm.bias""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            elif key_name.endswith("""/g""" ):
               UpperCAmelCase      					=  """model.blocks.%d.self_attn.norm.weight""" % player
               UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif (
             key_name.startswith("""model/wte""" )
             or key_name.startswith("""model/wpe""" )
             or key_name.startswith("""model/ete""" )
         ):
            UpperCAmelCase      					=  {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                key_name[-3:]
            ]
            UpperCAmelCase      					=  """model.%s.weight""" % nlayer
            UpperCAmelCase      					=  vnp.copy()  # same in embedded
            UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
            if key_name.startswith("""model/wte""" ):
               UpperCAmelCase      					=  """lm_head.weight"""
               UpperCAmelCase      					=  vnp.copy()  # same in embedded
               UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name.startswith("""model/wob""" ):
            UpperCAmelCase      					=  """final_logits_bias"""
            UpperCAmelCase      					=  vnp.copy()  # same in embedded
            UpperCAmelCase      					=  state.reshape((1, -1) )
            UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name == "model/dense/kernel":
            UpperCAmelCase      					=  """model.last_project.weight"""
            UpperCAmelCase      					=  vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
            UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
         elif key_name == "model/dense_1/bias":
            UpperCAmelCase      					=  """model.last_project.bias"""
            UpperCAmelCase      					=  vnp.copy()  # same because it is one dimensional
            UpperCAmelCase      					=  torch.tensor(UpperCamelCase_ )
   torch.save(UpperCamelCase_	,    args.output )


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser(
     description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
 parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
 __a	 =      parser.parse_args()
 convert_tf_gptsan_to_pt(args)








'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 							a__( snake_case__     ):
			a_	: int									=      '''efficientnet'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.5					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**_UpperCAmelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =dropout_rate
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(_UpperCAmelCase						) * 4





class 							a__( snake_case__     ):
			a_	: List[str]									=      version.parse('''1.11'''     )






			@property
			def 						_lowercase  (     self						)							->							Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					    ]						)






			@property
			def 						_lowercase  (     self						)							->							float:
					return 1E-5




